Development of surface roughness prediction model for high speed end milling of hardened tool steel

The quality of the surface plays a very important role performance of milling as a good-quality milled surface in a variety of manufacturing industries including the aerospace and automotive sectors where good quality surface significantly improves fatigue strength, corrosion resistance, or creep li...

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Main Authors:	Mohd Ali , Afifah, Adesta, Erry Yulian Triblas, Agusman, Delvis, Mohamad Badari, Siti Norbahiyah, Al Hazza, Muataz Hazza Faizi
Format:	Article
Language:	English
Published:	Asian Network for Scientific Information 2011
Subjects:	TS200 Metal manufactures. Metalworking
Online Access:	http://irep.iium.edu.my/7367/ http://irep.iium.edu.my/7367/ http://irep.iium.edu.my/7367/ http://irep.iium.edu.my/7367/1/AJAS_255-263.pdf

id	iium-7367
recordtype	eprints
spelling	iium-73672013-05-29T07:06:32Z http://irep.iium.edu.my/7367/ Development of surface roughness prediction model for high speed end milling of hardened tool steel Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi TS200 Metal manufactures. Metalworking The quality of the surface plays a very important role performance of milling as a good-quality milled surface in a variety of manufacturing industries including the aerospace and automotive sectors where good quality surface significantly improves fatigue strength, corrosion resistance, or creep life. This study discussed the issue of surface machined quality and the effort taken to predict surface roughness. For thus purpose , hardened materials AISI H13 tool steel with hardness of 48 Rockwell Hardness (HRC) was chosen for work material. Machining was done at High Cutting speed (Vc) from 150 up to 250 m/min, feedrate (Vf) 0005-0.15 mm/rev and depth of cut (DOC) 0.1-0.5mm. The analysisi and observation of the surface roughness were done by using optical surface roughness machine. Response Surface Methodology (RSM) Model was used to design the prediction model with parameters generated by using Central Composite Face (CCF) methods. A prediction model developed with 90% accuracy with the conclusion of feedrate as the main contributor to surface roughness followed by cutting speed. Therefore, RSM has been proven to be an efficient method to predict the surface finish during end-milling of H13 tool steel using TiAIN coated carbide tool inserts under dry conditions. Asian Network for Scientific Information 2011 Article PeerReviewed application/pdf en http://irep.iium.edu.my/7367/1/AJAS_255-263.pdf Mohd Ali , Afifah and Adesta, Erry Yulian Triblas and Agusman, Delvis and Mohamad Badari, Siti Norbahiyah and Al Hazza, Muataz Hazza Faizi (2011) Development of surface roughness prediction model for high speed end milling of hardened tool steel. Asian Journal of Scientific Research, 4 (3). pp. 255-263. ISSN 1992-1454 http://www.doaj.org/doaj?func=openurl&issn=19921454&genre=journal DOI : 10.3923/ajsr.2011.255.263
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TS200 Metal manufactures. Metalworking
spellingShingle	TS200 Metal manufactures. Metalworking Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi Development of surface roughness prediction model for high speed end milling of hardened tool steel
description	The quality of the surface plays a very important role performance of milling as a good-quality milled surface in a variety of manufacturing industries including the aerospace and automotive sectors where good quality surface significantly improves fatigue strength, corrosion resistance, or creep life. This study discussed the issue of surface machined quality and the effort taken to predict surface roughness. For thus purpose , hardened materials AISI H13 tool steel with hardness of 48 Rockwell Hardness (HRC) was chosen for work material. Machining was done at High Cutting speed (Vc) from 150 up to 250 m/min, feedrate (Vf) 0005-0.15 mm/rev and depth of cut (DOC) 0.1-0.5mm. The analysisi and observation of the surface roughness were done by using optical surface roughness machine. Response Surface Methodology (RSM) Model was used to design the prediction model with parameters generated by using Central Composite Face (CCF) methods. A prediction model developed with 90% accuracy with the conclusion of feedrate as the main contributor to surface roughness followed by cutting speed. Therefore, RSM has been proven to be an efficient method to predict the surface finish during end-milling of H13 tool steel using TiAIN coated carbide tool inserts under dry conditions.
format	Article
author	Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi
author_facet	Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi
author_sort	Mohd Ali , Afifah
title	Development of surface roughness prediction model for high speed end milling of hardened tool steel
title_short	Development of surface roughness prediction model for high speed end milling of hardened tool steel
title_full	Development of surface roughness prediction model for high speed end milling of hardened tool steel
title_fullStr	Development of surface roughness prediction model for high speed end milling of hardened tool steel
title_full_unstemmed	Development of surface roughness prediction model for high speed end milling of hardened tool steel
title_sort	development of surface roughness prediction model for high speed end milling of hardened tool steel
publisher	Asian Network for Scientific Information
publishDate	2011
url	http://irep.iium.edu.my/7367/ http://irep.iium.edu.my/7367/ http://irep.iium.edu.my/7367/ http://irep.iium.edu.my/7367/1/AJAS_255-263.pdf
first_indexed	2023-09-18T20:16:43Z
last_indexed	2023-09-18T20:16:43Z
_version_	1777407836998860800

Development of surface roughness prediction model for high speed end milling of hardened tool steel

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